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or a closely related subject, of which 30 credits must be at second-cycle level, including an independent project worth at least 15 credits, or equivalent knowledge are proficient in Swedish and/or
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closely related subject, of which 30 credits must be at second-cycle level, including an independent project worth at least 15 credits, or equivalent knowledge are proficient in Swedish and/or English
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subject or a closely related subject, of which 30 credits must be at second-cycle level, including an independent project worth at least 15 credits, or equivalent knowledge are proficient in Swedish and/or
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a closely related subject, of which 30 credits must be at second-cycle level, including an independent project worth at least 15 credits, or equivalent knowledge are proficient in Swedish and/or
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subject or a closely related subject, of which 30 credits must be at second-cycle level, including an independent project worth at least 15 credits, or equivalent knowledge are proficient in Swedish
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data analytics to health data, mainly from electronic health records. The project focuses on developing and evaluating machine learning models for predicting adverse drug events using both structured
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continued, much-needed development of e-health in healthcare. The doctoral student will contribute to the development of the research environment for this collaborative project and will be part of the